# Commodity futures and market efficiency

**Authors:** Ladislav Kristoufek, Miloslav Vosvrda  
**arXiv:** [1309.1492](https://arxiv.org/abs/1309.1492)  
**Date:** 2013-09-05  
**Summary page:** https://www.summarizepaper.com/en/paper/1309.1492v1/

## Key points

- Study analyzes market efficiency of 25 commodity futures across various groups
- Utilizes Efficiency Index to determine efficiency of commodities
- Heating oil identified as most efficient commodity, followed by WTI crude oil, cotton, wheat, and coffee
- Live cattle and feeder cattle identified as least efficient commodities
- Energy commodities deemed most efficient group, while other agricultural commodities (primarily livestock) deemed least efficient
- Long-term memory, fractal dimension, and approximate entropy contribute to overall inefficiency in commodity futures markets
- Positive relationship observed between fractal dimension and Hurst exponent suggests local persistence or trending in commodity prices connected to global anti-persistence
- Short-term predictability may exist but long-term prices tend to revert to fundamental values

## Summary

In their study titled "Commodity futures and market efficiency," Ladislav Kristoufek and Miloslav Vosvrda analyze the market efficiency of 25 commodity futures across various groups, including metals, energies, softs, grains, and other agricultural commodities. They utilize a recently proposed Efficiency Index to determine the efficiency of these commodities. The authors find that heating oil is the most efficient commodity among those analyzed, closely followed by WTI crude oil, cotton, wheat, and coffee. On the other end of the ranking, they identify live cattle and feeder cattle as the least efficient commodities. The efficiency levels are also found to be characteristic of specific groups of commodities; energy commodities are identified as the most efficient group while other agricultural commodities (primarily livestock) are deemed the least efficient. Additionally, Kristoufek and Vosvrda discuss the contributions of long-term memory, fractal dimension and approximate entropy to overall inefficiency in commodity futures markets. They discover a nonstandard relationship between fractal dimension and Hurst exponent; in their analyzed dataset they observe a positive relationship between these two measures which suggests that local persistence or trending in commodity prices is connected to global anti-persistence. The authors attribute this finding to the specific nature of commodity futures markets where short-term predictability may exist but long-term prices tend to revert to their fundamental values. Overall, this study provides valuable insights into market efficiency in commodity futures trading across different groups of commodities. The findings shed light on which commodities exhibit higher levels of efficiency and contribute to our understanding of long-term memory patterns and price dynamics in these markets.